More specifically, many computer vision and image processing problems are tackled with convolutional neural networks (CNNs) [1]}, [2]}, [3]}. Convolution neural networks have the property that they inherently respect, to some degree, translation symmetries. CNNs do not however take into account rotational or reflection symmetries. Cohen and Welling introduced group equivariant convolutional neural networks (G-CNNs) in [4]} and designed a classification network that is inherently invariant under 90 degree rotations, integer translations and vertical/horizontal reflections. Much work is being done on invariant/equivariant networks that exploit inherent symmetries, a non-exhaustive list is [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}. The idea of including geometric priors, such as symmetries, into the design of neural networks is called `Geometric Deep Learning' in [27]}.
